1. Field of the Invention
The present invention relates to a failure diagnosis method and a failure diagnosis apparatus for diagnosing a failure and malfunction of a drive mechanism section used in an image forming apparatus having a conveyance unit, such as a copying machine, a printer, a facsimile, or a complex machine having these functions, and other devices (for example, electric appliances and automobiles); an image forming apparatus to which the failure diagnosis method and the failure diagnosis apparatus are applied; a program for implementing the failure diagnosis method and the failure diagnosis apparatus by means of an electrical computer; and a storage (recording) medium storing the program.
More specifically, the invention relates to a technique for automatically diagnosing a conveyance device and an image forming apparatus by modeling factors that cause devices/apparatuses to fail into an information processing model based on probabilities such as the Bayesian network model.
2. Description of the Related Art
Recently, in office equipment such as, for example, a copying machine or a printer, high productivity is demanded, so that delays due to failures are not acceptable and it has been demanded that a failure is quickly detected and solved.
Also, in other industrial equipment such as automobiles, airplanes, robots, and semiconductor designing devices, a number of members that are highly reliable and operable at high speed with high accuracy are loaded as means for operation control.
Particularly, frequency of failure in drive members such as motors and solenoids and mechanical members that operate by interlocking with the drive members, including drive circuits for driving the motors, is high in comparison with other electronic parts (passive electronic parts such as resistors and capacitors, transistors, and ICs (integrated circuits)). Particularly, when the use environment is poor, even in a normal method of use, various abnormalities and failures that are hardly detected occur, and a large amount of labor is necessary for solving them.
Therefore, a method of automatic diagnosis with using a system (rule-type system) based on rules has been considered. As an example of the rule-type system, there is available a failure diagnosis system using the Bayesian network (for example, refer to U.S. Pat. No. 6,535,865).
According to U.S. Pat. No. 6,535,865, a system component that may cause the system to fail is modeled by using the Bayesian network, and the Bayesian network has an index node, plural cause nodes, and plural first troubleshooting nodes. The index node has a state indicating whether or not the system component has failed. The plural cause nodes are connected to the index node. Each of the cause nodes indicates a cause of the system component that cause a failure, the plural first troubleshooting nodes that are connected, respectively, to at least one of the plural cause nodes. The first troubleshooting nodes indicate troubleshooting steps for proposing actions for restoring the causes indicated by any of the connected cause nodes. With this configuration, when troubleshooting the system, an action that has a highest probability of solving the problem and requires the lowest cost estimated is proposed for a user.
Herein, in the configuration of U.S. Pat. No. 6,535,865, concretely, a service center has a server for a diagnosis system, and a customer executes diagnosis of his/her printer by using the Bayesian network while connecting to the server and exchanging data via the Internet.
In this example, a customer performs troubleshooting in a way that he/she answers questions from the diagnosis program. However, in order to acquire knowledge information, adopted is a method in which a human directly examines and obtains information by looking at a printer or a printed matter and inputs the information.
Therefore, in this method, if a customer who makes an examination does not get used to this method, there is fear that input information greatly varies, that an accurate diagnosis cannot be executed, and that serious false diagnosis may be caused. Furthermore, since many actions are assigned to a user, the user may feel a great deal of stress. In addition, there is a possibility that eliminating only a cause that is suggested before the action cannot solve the problem.